Identification of bile salt export pump inhibitors using machine learning: Predictive safety from an industry perspective

Abstract

Bile salt export pump (BSEP) is a transporter that moves bile salts from hepatocytes into bile canaliculi. BSEP inhibition can result in the toxic accumulation of bile salts in the liver, which has been identified as a risk factor of drug-induced liver injury (DILI). Since DILI is a frequent cause of drug withdrawals from the market or failings in drug development, in vitro BSEP activity is measured with the [³H]taurocholate uptake assay and a half-maximal inhibitory concentration (IC₅₀) higher than 30 µM is advised. Herein, a machine learning classification model was developed to accurately detect BSEP inhibitors and help in the prioritization of in vitro testing. Regression models for the numerical prediction of IC₅₀ values were also generated. Classification and regression models for BSEP inhibition have been evaluated on realistic settings, which is critical prior to ML-based decision making in drug discovery programs. This work illustrates how predictive safety can help in early toxicity risk assessment and compound prioritization by leveraging Novartis historical experimental data.